Download Relation of gene expression phenotype to immunoglobulin mutation genotype in B cell chronic lymphocytic leukemia.

Relation of Gene Expression Phenotype to Immunoglobulin
Mutation Genotype in B Cell Chronic
Lymphocytic Leukemia
Andreas Rosenwald,1 Ash A. Alizadeh,2 George Widhopf,5
Richard Simon,6 R. Eric Davis,1 Xin Yu,1 Liming Yang,1
Oxana K. Pickeral,1 Laura Z. Rassenti,5 John Powell,7 David Botstein,3
John C. Byrd,8 Michael R. Grever,9 Bruce D. Cheson,10
Nicholas Chiorazzi,11 Wyndham H. Wilson,12 Thomas J. Kipps,5
Patrick O. Brown,2, 4 and Louis M. Staudt1
1Metabolism
Abstract
The most common human leukemia is B cell chronic lymphocytic leukemia (CLL), a malignancy of mature B cells with a characteristic clinical presentation but a variable clinical course.
The rearranged immunoglobulin (Ig) genes of CLL cells may be either germ-line in sequence
or somatically mutated. Lack of Ig mutations defined a distinctly worse prognostic group of
CLL patients raising the possibility that CLL comprises two distinct diseases. Using genomicscale gene expression profiling, we show that CLL is characterized by a common gene expression “signature,” irrespective of Ig mutational status, suggesting that CLL cases share a common
mechanism of transformation and/or cell of origin. Nonetheless, the expression of hundreds of
other genes correlated with the Ig mutational status, including many genes that are modulated
in expression during mitogenic B cell receptor signaling. These genes were used to build a CLL
subtype predictor that may help in the clinical classification of patients with this disease.
Key words: cDNA microarrays • gene expression profiling • leukemia • lymphocytic •
chronic
Introduction
The observation that the rearranged Ig variable genes in
chronic lymphocytic leukemia (CLL)* cells can either be
unmutated or mutated suggested that CLL might comprise
two different diseases that have been lumped together using
Address correspondence to Louis M. Staudt, Metabolism Branch, National
Cancer Institute, Bldg. 10, Rm. 4N114, Bethesda, MD 20892. Phone:
301-402-1892; Fax: 301-496-9956; E-mail: [email protected]
*Abbreviations used in this paper: BCR, B cell receptor; CLL, chronic lymphocytic leukemia; DLBCL, diffuse large B cell lymphoma; PKC, protein
kinase C.
1639
The Journal of Experimental Medicine • Volume 194, Number 11, December 3, 2001 1639–1647
http://www.jem.org/cgi/content/full/194/11/1639
Downloaded from www.jem.org on March 13, 2006
Branch, Center for Cancer Research, National Cancer Institute, National Institutes of
Health, Bethesda, MD 20892
2Department of Biochemistry, the 3Department of Genetics and 4The Howard Hughes Medical
Institute, Stanford University School of Medicine, Stanford, CA 94305
5University of California at San Diego, Department of Medicine, La Jolla, CA 92093
6Biometric Research Branch, Division of Cancer Treatment and Diagnosis, National Cancer Institute,
National Institutes of Health, Bethesda, MD 20892
7Bioinformatics and Molecular Analysis Section, CBEL, CIT, National Institutes of Health,
Bethesda, MD 20892
8Department of Medicine, Walter Reed Army Medical Center, Washington, D.C. 20307
9Department of Internal Medicine, Ohio State University, Columbus, OH 43214
10CTEP, Division of Cancer Treatment and Diagnosis, National Cancer Institute, National Institutes
of Health, Bethesda, MD 20892
11North Shore-Long Island Jewish Research Institute, Manhasset, NY 11030
12Medicine Branch, Division of Clinical Sciences, National Cancer Institute, National Institutes of
Health, Bethesda, MD 20892
Materials and Methods
Microarray Procedures. Peripheral blood samples from CLL patients diagnosed according to National Cancer Institute guidelines
(8) were obtained after informed consent and were treated anonymously during microarray analysis. 33 CLL patients studied had
not received chemotherapy at the time of sample acquisition and
four patients had received prior treatment. Ig mutational status
was only studied in untreated patients. Leukemic cells from CLL
blood samples were purified by magnetic selection for CD19
(Miltenyi Biotec) at 4C before mRNA extraction and microarray analysis. Other mRNA samples from normal and malignant
lymphoid populations have been described previously as have cell
purification methods and array methods (6). All microarray experiments used the Cy5 dye to generate the experimental cDNA
probe from mRNA of normal and malignant lymphocytes, and
the Cy3 dye to generate the reference cDNA probe from mRNA
pooled from nine lymphoma cell lines as described previously (6).
1640
Expression data presented in Figs. 1, 4, and 5 are available at http:
//llmpp.nih.gov/cll.
Initial microarray data selection was based on fluorescence signal intensity. Each selected data point either had 100 relative fluorescent units (RFU’s) above background in both the Cy3 and Cy5
channels, or 500 RFU’s above background in either channel
alone. A supervised selection of genes preferentially expressed in
CLL cells (see Fig. 1 A) was performed as follows. First, we used
the fact that the majority of cell lines that were used to construct
the reference pool of mRNA were derived from DLBCL. The
percentage of CLL samples with expression ratio 3 relative to
the reference cell line pool was calculated, and the same calculation was also performed for the DLBCL samples. Genes were selected for which 50% of the CLL samples, and 25% of the
DLBCL samples, had ratios 3. Additionally, genes were selected
if the average CLL ratio was greater than the average DLBCL ratio by greater than threefold. For Fig. 1 B, representative genes
were chosen from Fig. 1 A by computing the average expression
in CLL samples and the average expression in resting B cell samples (adult and cord blood B cells). CLL signature genes were chosen to be at least twofold more highly expressed in CLL than in
resting B cells and CLL/resting B cell genes were chosen to be expressed equivalently (within twofold) in the two sample sets. Duplicate array elements representing the same genes were removed.
Germinal center genes were chosen from a previous analysis (6).
RT-PCR. 500 ng poly-A mRNA was used to generate first
strand cDNA using Superscript (Life Technologies) together with
random hexamers and oligo-dT primers. ZAP-70 oligonucleotide primers (5 TCTCCAAAGCACTGGGTG 3, 5 AGCTGTGTGTGGAGACAACCAAG 3) were then used for PCR
amplification for 27 cycles.
Statistical Analysis. A two-group t-statistic on log2 expression
ratios was used to measure the ability of each array element to
discriminate between the two CLL mutational subtypes univariately. For multivariate subtype prediction, we used a linear combination of log2 expression ratios for array elements that were
significant at the P 0.001 significance level in the univariate
analysis. The expression ratios were weighted in the linear combination by the univariate t statistics. The linear combination was
computed for each sample and the average linear combination
was computed for each CLL subtype. The midpoint of the two
CLL subtype means was used as a cut-point for subtype prediction. For the cross-validation analysis, the subtype predictor was
calculated by sequentially omitting one sample from the test set of
cases, and using the remaining cases to generate the predictor. In
Fig. 4 B, calculation of the P value from the permutation distribution of the t-statistic also demonstrated the high statistical significance of the differential gene expression between the CLL
subtypes (data not shown). Classification was determined on all
CLL cases with the exception of CLL-60 (Ig-unmutated) and
CLL-21 and CLL-51 (minimally mutated cases).
In Fig. 5, the choice of B cell activation genes was made as follows. The B cell activation series of microarray experiments included several different stimulations with anti-IgM for 6, 24, and
48 h for each Lymphochip array element, we averaged the data at
each activation time point, and then selected those elements that
gave a twofold induction compared with the resting B cell average for at least one time point.
Results
The Gene Expression Signature of CLL. We profiled
gene expression in CLL samples (n 37) using Lymphochip
Gene Expression Profiling in Chronic Lymphocytic Leukemia
Downloaded from www.jem.org on March 13, 2006
standard diagnostic methods (1–3). Somatic hypermutation
of Ig genes is a specialized diversification mechanism that is
activated in B cells at the germinal center stage of differentiation (4, 5). Thus, it was suggested that CLL might include
two disparate malignancies, one derived from an Ig-unmutated, pregerminal center B cell, and the other from an Igmutated B cell that has passed through the germinal center.
This “two disease” model of CLL was further supported by
the observation that Ig-unmutated and Ig-mutated CLL patients had distinctly different clinical courses (2, 3). This
model predicts that Ig-unmutated and Ig-mutated CLL
would not be highly related to each other in gene expression. A precedent for this model is found in the recent
demonstration that another lymphoid malignancy, diffuse
large B cell lymphoma (DLBCL), actually includes two distinct diseases that are morphologically indistinguishable but
which have largely nonoverlapping gene expression profiles
(6). An alternative hypothesis is that all cases of CLL have a
common cellular origin and/or a common mechanism of
malignant transformation. This model predicts that Igmutated and Ig-unmutated CLL cases should share a gene
expression signature that is characteristic of CLL.
To test these two models, and to identify molecular differences between CLL patients that might influence their
clinical course, we determined the gene expression phenotype of CLL on a genomic scale using Lymphochip cDNA
microarrays (6, 7). Our data demonstrate that CLL, irrespective of the Ig mutational status, is defined by a characteristic gene expression signature, thus favoring the notion
that all cases share some aspects of pathogenesis. Nonetheless, we found hundreds of genes differentially expressed
between Ig-unmutated and Ig-mutated CLL providing the
first molecular insight into the biological mechanisms that
lead to the divergent clinical behaviors of these subgroups
of CLL patients. The unexpected finding that B cell activation genes were differentially expressed between the two
Ig-mutational subgroups in CLL suggests the intriguing
possibility that signaling pathways downstream of the B cell
receptor (BCR) contribute to the more aggressive clinical
behavior of the Ig-unmutated subtype.
ter B cells (Fig. 1 C). In addition to this set of CLL signature
genes, CLL preferentially expressed a set of genes that distinguish resting, G0 stage blood B cells from mitogenically activated blood B cells and germinal center B cells that are traversing the cell cycle (Fig. 1 B). The expression of these
resting B cell genes by CLL cells is consistent with the indolent, slowly proliferating character of this malignancy.
One of these resting B cell samples was prepared from
human cord blood that is enriched for B cells bearing the
CD5 surface marker, a B cell subpopulation that has been
proposed to be the normal counterpart of CLL. The cord
blood B cells were 80% CD5 by FACS® analysis (data
not shown) whereas resting B cells from adult blood are
10–20% CD5 (9). We did not observe notably higher expression of the CLL signature genes in the cord blood B
cell sample than in the adult B cell sample (Fig. 1) and no
overall correlation in the expression of genes in Fig. 1 was
observed between CLL and either adult or cord blood B
cells (Pearson correlation coefficients 0.27 and –0.21, respectively). Thus, our gene expression profiling analysis
does not provide support for the hypothesis that the CD5
B cell is a CLL precursor. It is certainly possible, however,
that the expression of the CLL signature genes might be
due to the oncogenic mechanisms of CLL and therefore
might not be a feature of any normal B cell subpopulation.
Figure 1. Discovery of a common
gene expression phenotype in CLL.
(A) 328 Lymphochip array elements
representing 247 genes that were
more highly expressed as mRNA in
the majority of CLL samples relative
to DLBCL samples. The expression
data are presented as a matrix in
which the rows represent individual
genes, and the columns represent individual mRNA samples. The relative level of gene expression is depicted according to the color scale
shown at the bottom. Gray squares
indicate missing or excluded data.
(B) Relative expression levels of selected genes from A in mRNA samples presented in the following order,
from left to right: cell lines (JVM-HH,
OCI-Ly10, OCI-Ly3, U937); T cells
(adult, CD4, unstimulated; neonatal, CD4, unstimulated; fetal,
CD4, unstimulated; adult, CD4,
PMA [P] and ionomycin [I]; neonatal cord blood T cells, P and I;
fetal, CD4, P and I), resting
B-cells (cord blood CD19 B cells;
adult blood CD19 B cells), activated B cells (adult blood B cells,
anti-IgM CD40L 6 h; adult blood
B cells, anti-IgM 24 h; adult blood B
cells, anti-IgM CD40L 24 h; adult
blood B cells, anti-IgM IL-4
24 h), adult blood memory B-cells
(CD27), tonsil germinal center B
cells, follicular lymphomas (n 7),
DLBCLs (n 40), and CLL (n 37). The CLL samples were grouped according to Ig mutational status as indicated. In the gene names, an asterisk denotes a sequence verified gene, IM indicates an IMAGE consortium (reference 29) clone identification number, and LC indicates an unsequenced Lymphochip clone identification number. (C) Relative expression levels of selected genes characteristic of the germinal center B cell differentiation stage.
1641
Rosenwald et al.
Downloaded from www.jem.org on March 13, 2006
cDNA microarrays containing 17,856 human cDNAs (7).
To facilitate comparison of each CLL mRNA sample with
the others and with previously generated data sets, we compared gene expression in each CLL mRNA sample to a
common reference mRNA pool prepared from lymphoid
cell lines (6, 7). Using this strategy, the relative gene expression in the CLL cases could be compared with other B cell
malignancies (DLBCL and follicular lymphoma) and of normal B cell and T cell subpopulations. Fig. 1 A presents expression data from 328 Lymphochip array elements representing 247 genes that were selected in a supervised
fashion (see Materials and Methods) to be more highly expressed in the majority of CLL samples than in DLBCL
samples (n 40). These genes fall into two broad categories,
which are highlighted by representative genes in Fig. 1 B.
Genes in the first category define a CLL gene expression
“signature” that distinguishes CLL from various normal B
cell subsets and from other B cell malignancies. The CLL
signature genes were not expressed highly in resting blood B
cells or in germinal center B cells. This group of genes includes several named genes not previously suspected to be
expressed in CLL (e.g., Wnt3, titin, Ror1) as well as a number of novel genes from various normal and malignant B cell
cDNA libraries. By contrast, CLL cells lacked expression of
most genes that are preferentially expressed in germinal cen-
nificantly worse clinical course, requiring earlier treatment,
than the Ig-mutated CLL patients (Fig. 2 B), in keeping
with previous reports (2, 3).
CLL Subtype Distinction Genes. Given the dramatically
different clinical behavior of the Ig-unmutated and Igmutated CLL patients, it was evident that gene expression
differences should be discernible between these groups. To
both discover such genes and statistically validate their relationship to the Ig-mutational subgroups, we conducted the
Ig mutational analysis independently and sequentially in
two random subsets of our CLL patients (Fig. 3). The
“training” set consisted of 10 Ig-unmutated cases and eight
Ig-mutated cases. In this gene discovery phase, we assigned
the minimally mutated CLL cases to the mutated class. The
mean expression of each gene was then calculated for both
mutational subgroups and the statistical significance of the
difference of these means was determined. All genes that
discriminated between the mutational subgroups at a significance of P 0.001 (n 56) were used to form a “predictor” that could be used to assign a CLL sample to a mutational subgroup based on gene expression (see Methods).
The performance of this CLL subtype predictor was initially tested using a cross-validation strategy (Fig. 3 A). One
of the 18 CLL samples in the training set was omitted, the
statistically significant genes were determined, and a predictor was calculated based on the remaining 17 samples. The
omitted sample was then assigned to a CLL subtype based
Figure 2. Analysis of somatic mutations
in the Ig VH genes in 28 CLL patients and
correlation with their clinical courses. (A)
VH gene usage and distribution of replacement () and silent (*) mutations in the
complementarity
determining
regions
(CDRs) and framework regions (FWs). #,
VH sequence most homologous to multiple
cDNA sequences. (B) Kaplan-Meier curve
comparing the time from diagnosis to treatment between CLL patients with mutated
and unmutated VH genes. Median time to
treatment in Ig-mutated CLL: 95 mo; median time to treatment in Ig-unmutated
CLL: 28 mo. The difference is significant at
the P 0.001 level (log-rank test).
1642
Gene Expression Profiling in Chronic Lymphocytic Leukemia
Downloaded from www.jem.org on March 13, 2006
Ig Mutational Status. The expressed Ig heavy chain
genes were sequenced from 28 CLL cases and compared
with known germ-line encoded Ig VH segments as described previously (10) (Fig. 2 A). By convention, VH sequences that matched known germ line sequences with
98% identity were considered unmutated, as any minor
differences observed in this group were assumed to reflect
genetic polymorphism (1–3). By this criterion, 16 CLL
cases in our study set were unmutated. The remaining
cases were further separated into a group of 10 highly mutated cases (97% identity with any germ-line VH segment) and a group of two cases that were minimally mutated (97% but 98% identity with known germ-line
VH genes). CLL cases were grouped in Fig. 1 according to
Ig mutational status as indicated. Although some variation
in expression of the CLL signature and CLL/resting B cell
genes was evident between CLL patients, most patients in
each Ig mutational subtype highly expressed these genes at
comparable levels. Furthermore, an unsupervised hierarchical clustering of the CLL cases using 10,249 Lymphochip array elements resulted in a clustering dendrogram in
which the Ig-unmutated and Ig-mutated CLL cases were
extensively intermingled (data not shown). Thus, the overall gene expression profiles of the two CLL subtypes were
largely overlapping.
Segregation of our patients according to Ig mutational
status revealed that Ig-unmutated CLL patients had a sig-
on gene expression using this predictor. The Ig mutational
status of 17 CLL samples was correctly assigned by this procedure with one misassignment. To test the statistical significance of this result, we created 1,000 random permutations of the assignments of CLL samples to the Ig mutation
subgroups. For each permutation, the cross-validation process described above was repeated. Only one of the 1,000
random permutations generated a predictor that performed
as well as the predictor based on the unpermutated data,
demonstrating that the significance of the gene expression
difference between the CLL subtypes was P 0.001.
As a final test of the CLL subtype predictor, we determined the Ig mutational status of a “test” set of 10 additional CLL cases and used the predictor derived from the
training set to assign the cases in this test set to a CLL subtype based on gene expression in a blinded fashion (Fig. 3
B). Nine out of ten of the test cases were correctly assigned, showing the ability of the CLL subtype predictor to
correctly assign new CLL cases based on gene expression
data that was not used to generate the predictor. The one
misclassified CLL case (CLL-60) clearly was an outlier in
gene expression (see below). Taken together with the
cross-validation results, these data demonstrate that gene
expression can define CLL subtypes that have different degrees of Ig mutation.
1643
Rosenwald et al.
Downloaded from www.jem.org on March 13, 2006
Figure 3. Statistical methodology for the creation and validation of an
Ig-mutational status predictor in CLL. (A) Performance of the predictor
using a cross-validation strategy. (B) Performance of the Ig-mutational
subtype predictor in a test set of six unmutated (*) and four mutated CLL
() samples.
An important practical benefit of these findings would be
to create a diagnostic test for the CLL subtypes based upon
gene expression. In this regard, one of the most differentially expressed genes from the analysis of the training set of
cases, ZAP-70, could classify all of the cases in both the
training and the test set with 100% accuracy. Likewise, predictors based on two genes (ZAP-70 and IM1286077) or
three genes (ZAP-70, IM1286077, activation-induced
C-type lectin) discovered using the training set formed
CLL subtype predictors that performed with 100% accuracy on the training set and test set of CLL cases.
We next expanded our search for CLL subtype distinction genes using data from both the training set and test set
of CLL cases. The two CLL cases with minimal Ig mutations (CLL-22 and CLL-51) were excluded based on the
possibility that their Ig sequences might actually represent
as yet undescribed polymorphic VH alleles. CLL-60 was excluded based on its unusual gene expression characteristics
that led to its misclassification by the CLL subtype predictor. Fig. 4 A presents 205 Lymphochip array elements
(175 genes) that were differentially expressed between
the CLL subtypes with a statistical significance of P 0.001. Hierarchical clustering of the CLL cases based on
expression of these genes placed the majority of Ig-unmutated CLL cases in one cluster and the Ig-highly mutated
CLL cases in another. As expected, CLL-60 was more
closely aligned with the Ig-mutated CLL cases, though it
was an outlier from the major cluster of Ig-mutated CLL
cases. Interestingly, both of the CLL cases with a low Ig
mutational load were also outliers, though they were more
closely related to the Ig-mutated CLL subtype than to the
Ig-unmutated CLL subtype. These data define two predominant CLL subtypes that differ in the expression of
hundreds of genes but also demonstrate that additional minor CLL subtypes may exist that have distinct gene expression profiles. Fig. 4 B highlights some of the genes that
most strongly differentiate between the CLL subtypes.
ZAP-70 was the most tightly discriminating gene, with an
average 4.3-fold higher expression in Ig-unmutated CLL
than in Ig-mutated CLL (P 106). RT-PCR analysis
confirmed ZAP-70 expression in two Ig-unmutated CLL
cases (CLL-48 and CLL-49), in contrast to CLL-66 and
CLL-69 that were Ig-mutated (Fig. 4 C). Surprisingly,
ZAP-70 expression was also observed in several B cell lines
(LILA, LK-6, OCI-Ly2), but not in many others (Raji;
Fig. 4 C, and data not shown).
Relationship between B Cell Activation and the CLL Subtype
Distinction. Several of the CLL subtype distinction genes
are known or suspected to be induced by protein kinase C
(PKC) signaling, including activation-induced C-type lectin (11), MDS019, a very close paralogue of phorbolin
1 (12), and gravin, a scaffold protein that binds PKC and
may regulate its activity (13). One mechanism by which
PKC is activated in B cells is through BCR signaling (14).
Therefore, we investigated whether the CLL subtype distinction genes are regulated during activation of blood B
cells, using a gene expression database generated previously
using Lymphochip microarrays (6). Strikingly, many of the
genes that were more highly expressed in Ig-unmutated
CLL were induced during activation of blood B cells (Fig.
5 A). Many of these genes encode proteins involved in cell
cycle control (e.g., cyclin D2) or in cellular metabolism required for cell cycle progression (e.g., HPRT and other
1644
nucleotide modifying enzymes). Conversely, the majority
of the genes that were expressed at lower levels in Igunmutated CLL were strongly downmodulated during B
cell activation (Fig. 5 B). These results demonstrate that the
CLL subtype distinction genes are enriched for genes that
Gene Expression Profiling in Chronic Lymphocytic Leukemia
Downloaded from www.jem.org on March 13, 2006
Figure 4. Relative gene expression levels of CLL subtype distinction genes. (A) Hierarchical clustering of gene expression data for
205 array elements representing
175 genes that were differentially expressed between mutated
and unmutated CLL samples (P 0.001). (B) Hierarchical clustering
of genes that most strongly discriminated between the CLL subtypes. Also shown for each gene is
the ratio of mean expression of the
gene in Ig-unmutated CLL samples (excluding CLL-60) versus
mean expression in Ig-mutated
(high) CLL samples, together with
the P values (Student’s t test) that
quantitate the significance of the
difference in mean expression between the two CLL subtypes. (C)
RT-PCR analysis of ZAP-70 expression. Shown are data from two
Ig-unmutated and two Ig-mutated
CLL cases, a T cell line (Jurkat),
various B cell lines found by microarray analysis to express ZAP-70
(LILA, LK6, OCI-Ly2), and a B
cell line not expressing ZAP-70
(Raji; C, and data not shown). The
control lane represents a reaction
in which the reverse transcriptase
was omitted.
Discussion
are modulated in expression by B cell activation. Indeed,
47% of the CLL subtype distinction genes were induced
during B cell activation, whereas only 18% of all Lymphochip genes were in this category (Fig. 5 C).
1645
Rosenwald et al.
Downloaded from www.jem.org on March 13, 2006
Figure 5. (A and B) Response of CLL subtype distinction genes during
B cell activation. Gene expression data from the following B cell samples is
depicted: 1, gene expression average from three resting B cell samples; 2,
blood B cells, anti-IgM 6 h; 3, blood B cells, anti-IgM CD40L 6 h; 4,
blood B cells, anti-IgM IL-4; 5, blood B cells, anti-IgM CD40L IL-4; 6, blood B cells, anti-IgM 24 h; 7, blood B cells, anti-IgM CD40L
24 h; 8, blood B cells, anti-IgM IL-4 24 h; 9, blood B cells, anti-IgM CD40L IL-4 24 h; 10–11, blood B cells, anti-IgM CD40L IL-4
48 h. (C) Percentage of CLL subtype distinction genes (red bar) and all
‘Lymphochip’ genes (blue bar) that are induced during B cell activation.
The comprehensive profiling of gene expression in CLL
presented here provides a new molecular framework for
understanding the etiology of this leukemia and the divergent clinical courses of these patients. Using genomic-scale
gene expression profiling, we addressed a current controversy in CLL pathogenesis, namely whether this diagnosis
comprises more than one disease entity. CLL patients have
been subdivided based on the Ig mutational status of their
leukemic cells (1–3), but it was unclear whether these patients had molecularly distinct diseases. Our data demonstrate that all CLL patients share a characteristic gene expression signature in their leukemic cells. These findings
support a model in which all cases of CLL have a common
cell of origin and/or a common mechanism of malignant
transformation. In this model, the CLL-specific gene expression signature might represent the gene expression signature of a common normal precursor cell or it might reflect the downstream gene expression consequences of a
common oncogenic event. These findings are in contrast to
the previous observation that DLBCL consists of two disease entities that did not have overlapping gene expression
outside of genes involved in proliferation and in the host
response to the tumor (6).
Previously unsuspected features of CLL biology emerge
from its gene expression profile, generating a wealth of hypotheses to guide future studies of this disease. CLL cells
proliferate slowly in vivo, driven by unknown signals.
Therefore, it is notable that Wnt-3 was highly, and selectively, expressed in CLL (Fig. 1 B). The Wnt gene family
encodes secreted proteins that signal through cell surface
receptors of the frizzled family to control development and
mediate malignant transformation (15). Intriguingly, another CLL signature gene, Ror1, encodes a receptor tyrosine kinase with an extracellular domain that resembles a
Wnt interaction domain of frizzled (16). Recently, Wnt-3
has been shown to promote proliferation of mouse bone
marrow pro-B cells by initiating signaling events leading to
transcriptional activation by LEF-1(17). Thus, CLL cells
may use an autocrine mechanism of proliferation that is
used normally by B cell progenitors.
We nevertheless also found that the expression of hundreds of other genes correlated with the Ig mutational status in CLL, providing insights into the biological mechanisms that lead to the divergent clinical behaviors of CLL
patients. The most differentially expressed gene between
the CLL subtypes was ZAP-70, a critical kinase that transduces signals from the T cell antigen receptor, and is preferentially expressed in normal T lymphocytes (18). Differential expression of ZAP-70 between CLL subtypes was
therefore surprising since its expression in normal B cells
has not been previously reported. However, by microarray
analysis and RT-PCR analysis we found that ZAP-70
mRNA is highly expressed in some B lymphoma cell lines
along with being differentially expressed by the CLL subtypes. A ZAP-70–related kinase, syk, transduces signals
from the BCR (19), raising the possibility that ZAP-70
1646
targeting of these signaling pathways could specifically benefit those CLL patients that show gene expression evidence
that these pathways are active.
We thank the Cancer Genome Anatomy Project (CGAP), led by
Bob Strausberg and Rick Klausner, for help in constructing the
Lymphochip microarray, and Christa Prange for providing CGAP
cDNA clones. We also thank Rick Klausner for helpful discussions.
A. Rosenwald was supported by the Deutsche Krebshilfe, Bonn,
Germany. Research at Stanford was supported by grants from the
National Cancer Institute to D. Botstein and P.O. Brown, who is
an Associate Investigator of the Howard Hughes Medical Institute.
A. Alizadeh was initially supported by the Howard Hughes Medical
Institute Research Scholar Program while at the National Institutes
of Health and then by the Medical Scientist Training Program at
Stanford University. This work was also supported by grants from
the National Cancer Institute to T.J. Kipps and N. Chiorazzi
(RO1CA 81554 and RO1CA 87956) and to the CLL Research
Consortium.
Submitted: 1 August 2001
Revised: 23 August 2001
Accepted: 28 August 2001
References
1. Fais, F., F. Ghiotto, S. Hashimoto, B. Sellars, A. Valetto, S.L.
Allen, P. Schulman, V.P. Vinciguerra, K. Rai, L.Z. Rassenti,
et al. 1998. Chronic lymphocytic leukemia B cells express restricted sets of mutated and unmutated antigen receptors. J.
Clin. Invest. 102:1515–1525.
2. Hamblin, T.J., Z. Davis, A. Gardiner, D.G. Oscier, and F.K.
Stevenson. 1999. Unmutated Ig V(H) genes are associated
with a more aggressive form of chronic lymphocytic leukemia. Blood. 94:1848–1854.
3. Damle, R.N., T. Wasil, F. Fais, F. Ghiotto, A. Valetto, S.L.
Allen, A. Buchbinder, D. Budman, K. Dittmar, J. Kolitz, et
al. 1999. Ig V gene mutation status and CD38 expression as
novel prognostic indicators in chronic lymphocytic leukemia.
Blood. 94:1840–1847.
4. Jacob, J., G. Kelsoe, K. Rajewsky, and U. Weiss. 1991. Intraclonal generation of antibody mutants in germinal centers.
Nature. 354:389–392.
5. Klein, U., T. Goossens, M. Fischer, H. Kanzler, A. Braeuninger, K. Rajewsky, and R. Kuppers. 1998. Somatic hypermutation in normal and transformed human B cells. Immunol.
Rev. 162:261–281.
6. Alizadeh, A.A., M.B. Eisen, R.E. Davis, C. Ma, I.S. Lossos,
A. Rosenwald, J.C. Boldrick, H. Sabet, T. Tran, X. Yu, et
al. 2000. Distinct types of diffuse large B-cell lymphoma
identified by gene expression profiling. Nature. 403:503–511.
7. Alizadeh, A., M. Eisen, R.E. Davis, C. Ma, H. Sabet, T.
Tran, J. Powell, L. Yang, G. Marti, T. Moore, et al. 1999.
The Lymphochip: a specialized cDNA microarray for the genomic-scale analysis of gene expression in normal and malignant lymphocytes. Cold Spring Harbor Symp. Quant. Biol. 64:
71–78.
8. Cheson, B.D., J.M. Bennett, M. Grever, N. Kay, M.J. Keating, S. O’Brien, and K.R. Rai. 1996. National Cancer Institute-sponsored working group guidelines for chronic lymphocytic leukemia: revised guidelines for diagnosis and
treatment. Blood. 87:4990–4997.
9. Geiger, K.D., U. Klein, A. Brauninger, S. Berger, K. Leder,
Gene Expression Profiling in Chronic Lymphocytic Leukemia
Downloaded from www.jem.org on March 13, 2006
might alter BCR signaling in CLL cells. Another CLL subtype distinction gene, Pak1, could contribute to the resistance of CLL cells to apoptosis by phosphorylating Bad and
thereby preventing Bad from inhibiting BCL-2 (20).
FGFR1 is a receptor tyrosine kinase that can stimulate cellular proliferation after interaction with fibroblast growth
factors. The higher expression of FGFR1 in Ig-unmutated
CLL is intriguing given that CLL patients have elevated
blood levels of basic fibroblast growth factor which can activate FGFR1 and block apoptosis in CLL (21, 22).
Intriguingly, CLL subtype distinction genes were enriched for genes that are modulated in expression during
signaling of B cells through the BCR. One hypothesis
raised by this observation is that the leukemic cells in Igunmutated CLL may have ongoing BCR signaling. Interestingly, the VH repertoire usage in the Ig-unmutated and
Ig-mutated CLL is distinct (1–3) and the combinations of
VH, DH, and JH gene segments rearranged in CLL cells are
not random (1–3, 23, 24). These observations suggest that
the surface Ig receptors of CLL cells may have specificity
for unknown environmental or self-antigens. Indeed, CLL
cells have been shown to frequently produce antibodies
that bind classical autoantigens (25–27). The gene expression profiling data presented in this report raise the possibility that Ig-unmutated CLL cells may be continuously stimulated in vivo by antigen, giving rise to a gene expression
profile that is reminiscent of BCR signaling. Indeed, CLL
cells from patients with progressive disease were more
readily stimulated by BCR cross-linking to synthesize
DNA than were CLL cells from patients with stable disease
(28). Although this study did not distinguish between Igunmutated and Ig-mutated CLL, the results are consistent
with a differential ability of these subtypes to signal through
the BCR. Alternatively, it is possible that Ig-unmutated
CLL cells activate the same signaling pathways that are engaged during B cell activation as a result of genetic changes
in the leukemic cells or by other pathological mechanisms.
An immediate clinical application of the present results
would be in the differential molecular diagnosis of CLL.
We demonstrated that as few as 1–3 genes could correctly
assign patients to a CLL subtype with 100% accuracy.
Thus, our results could be used to establish a quantitative
RT-PCR test to diagnose the CLL subtypes and that
would be easier to adopt clinically than DNA sequence
analysis of Ig variable regions. Given the relatively benign
course of Ig-mutated CLL, a simple diagnostic test based
on gene expression would provide valuable prognostic information for CLL patients and could be used to guide
treatment decisions.
Finally, our results suggest new therapeutic approaches
to this currently incurable leukemia. First, the protein
products of some of the CLL signature genes may present
new targets for mAb therapy and for vaccine approaches to
CLL. Second, the unexpected finding that B cell activation
genes were upregulated in Ig-unmutated CLL patients suggests the intriguing possibility that signaling pathways
downstream of the BCR may contribute to the more progressive clinical course of these patients. Thus, therapeutic
10.
11.
12.
13.
14.
16.
17.
18.
19.
1647
Rosenwald et al.
20. Schurmann, A., A.F. Mooney, L.C. Sanders, M.A. Sells,
H.G. Wang, J.C. Reed, and G.M. Bokoch. 2000. p21-activated kinase 1 phosphorylates the death agonist bad and protects cells from apoptosis. Mol. Cell. Biol. 20:453–461.
21. Aguayo, A., H. Kantarjian, T. Manshouri, C. Gidel, E. Estey,
D. Thomas, C. Koller, Z. Estrov, S. O’Brien, M. Keating, et
al. 2000. Angiogenesis in acute and chronic leukemias and
myelodysplastic syndromes. Blood. 96:2240–2245.
22. Menzel, T., Z. Rahman, E. Calleja, K. White, E.L. Wilson,
R. Wieder, and J. Gabrilove. 1996. Elevated intracellular
level of basic fibroblast growth factor correlates with stage of
chronic lymphocytic leukemia and is associated with resistance to fludarabine. Blood. 87:1056–1063.
23. Widhopf, G.F. II, and T.J. Kipps. 2001. Normal B cells express 51p1-encoded Ig heavy chains that are distinct from
those expressed by chronic lymphocytic leukemia B cells. J.
Immunol. 166:95–102.
24. Johnson, T.A., L.Z. Rassenti, and T.J. Kipps. 1997. Ig VH1
genes expressed in B cell chronic lymphocytic leukemia exhibit distinctive molecular features. J. Immunol. 158:235–246.
25. Borche, L., A. Lim, J.L. Binet, and G. Dighiero. 1990. Evidence that chronic lymphocytic leukemia B lymphocytes are
frequently committed to production of natural autoantibodies. Blood. 76:562–569.
26. Sthoeger, Z.M., M. Wakai, D.B. Tse, V.P. Vinciguerra, S.L.
Allen, D.R. Budman, S.M. Lichtman, P. Schulman, L.R.
Weiselberg, and N. Chiorazzi. 1989. Production of autoantibodies by CD5-expressing B lymphocytes from patients with
chronic lymphocytic leukemia. J. Exp. Med. 169:255–268.
27. Broker, B.M., A. Klajman, P. Youinou, J. Jouquan, C.P.
Worman, J. Murphy, L. Mackenzie, R. Quartey-Papafio, M.
Blaschek, P. Collins, et al. 1988. Chronic lymphocytic leukemic (CLL) cells secrete multispecific autoantibodies. J. Autoimmun. 1:469–481.
28. Aguilar-Santelises, M., H. Mellstedt, and M. Jondal. 1994.
Leukemic cells from progressive B-CLL respond strongly to
growth stimulation in vitro. Leukemia. 8:1146–1152.
29. Lennon, G., C. Auffray, M. Polymeropoulos, and M.B.
Soares. 1996. The I.M.A.G.E. Consortium: an integrated
molecular analysis of genomes and their expression. Genomics.
33:151–152.
Downloaded from www.jem.org on March 13, 2006
15.
K. Rajewsky, M.L. Hansmann, and R. Kuppers. 2000. CD5positive B cells in healthy elderly humans are a polyclonal B
cell population. Eur. J. Immunol. 30:2918–2923.
Bessudo, A., V. Cherepakhin, T.A. Johnson, L.Z. Rassenti,
E. Feigal, and T.J. Kipps. 1996. Favored use of immunoglobulin V(H)4 Genes in AIDS-associated B-cell lymphoma.
Blood. 88:252–260.
Hamann, J., K.T. Montgomery, S. Lau, R. Kucherlapati, and
R.A. van Lier. 1997. AICL: a new activation-induced antigen encoded by the human NK gene complex. Immunogenetics. 45:295–300.
Madsen, P., S. Anant, H.H. Rasmussen, P. Gromov, H.
Vorum, J.P. Dumanski, N. Tommerup, J.E. Collins, C.L.
Wright, I. Dunham, et al. 1999. Psoriasis upregulated phorbolin-1 shares structural but not functional similarity to the
mRNA-editing protein apobec-1. J. Invest. Dermatol. 113:
162–169.
Nauert, J.B., T.M. Klauck, L.K. Langeberg, and J.D. Scott.
1997. Gravin, an autoantigen recognized by serum from myasthenia gravis patients, is a kinase scaffold protein. Curr. Biol.
7:52–62.
Cambier, J.C., C.M. Pleiman, and M.R. Clark. 1994. Signal
transduction by the B cell antigen receptor and its coreceptors. Annu. Rev. Immunol. 12:457–486.
Polakis, P. 2000. Wnt signaling and cancer. Genes Dev. 14:
1837–1851.
Saldanha, J., J. Singh, and D. Mahadevan. 1998. Identification of a Frizzled-like cysteine rich domain in the extracellular region of developmental receptor tyrosine kinases. Protein
Sci. 7:1632–1635.
Reya, T., M. O’Riordan, R. Okamura, E. Devaney, K. Willert, R. Nusse, and R. Grosschedl. 2000. Wnt signaling regulates B lymphocyte proliferation through a LEF-1 dependent
mechanism. Immunity. 13:15–24.
Chu, D.H., C.T. Morita, and A. Weiss. 1998. The Syk family of protein tyrosine kinases in T-cell activation and development. Immunol. Rev. 165:167–180.
Turner, M., E. Schweighoffer, F. Colucci, J.P. Di Santo, and
V.L. Tybulewicz. 2000. Tyrosine kinase SYK: essential functions for immunoreceptor signalling. Immunol. Today. 21:
148–154.